Tangyu Fu
Papers
1
Total Citations
6
H-Index
1
About
Tangyu Fu is a researcher advancing the application of computer vision and deep learning to critical infrastructure monitoring. Their primary research area focuses on intelligent defect detection for electrical substation equipment, addressing the significant challenge of inspecting remote or inaccessible facilities where traditional methods like drones or ground robots fall short. Fu’s most-cited work, "Defect Detection Algorithm for Electrical Substation Equipment Based on Improved YOLOv10n" (2025, 6 citations), introduces a novel enhancement to the state-of-the-art YOLOv10n object detection framework. This contribution directly tackles the practical limitations of automated inspection in complex, real-world environments, offering a more reliable and efficient solution for identifying equipment anomalies. By improving detection accuracy and robustness, Fu’s research supports the transition toward safer, more autonomous maintenance of essential power grid assets. Their work is particularly notable for bridging the gap between cutting-edge algorithmic development and pressing operational needs in the energy sector, demonstrating a clear commitment to applied research with tangible societal impact.
Research Focus
Key Achievements
Top Papers
- 1